In Detail

Neo4j is producing a lot curiosity between NoSQL database clients for its positive factors, functionality and scalability, and robustness. The software program additionally offers clients with a really ordinary and expressive graph version and ACID transactions with rollbacks. despite the fact that, using Neo4j in a real-world undertaking should be tricky in comparison to a standard relational database. Cypher fills this hole with SQL, offering a declarative syntax and the expressiveness of trend matching. This rather uncomplicated yet robust language enables you to concentrate on your area rather than getting misplaced in database entry. As you'll research during this booklet, very complex database queries can simply be expressed via Cypher.

This publication is a pragmatic, hands-on consultant to designing, imposing, and querying a Neo4j database speedy and painlessly. via a few functional examples, this publication uncovers all of the behaviors to help you to use Neo4j successfully, with assistance and tips to assist you alongside the way in which. The publication begins with the elemental clauses and styles to accomplish read-only queries with Cypher. you'll then find out about clauses and guidance that may be used with styles to tricky effects coming from development matching. subsequent, you are going to grasp the clauses required to change a graph. after you have bought those fundamentals correct with assistance from functional examples, you'll then know about instruments and practices to enhance the functionality of queries and the way emigrate a database to Neo4j from the floor up. to complete off, the publication covers Cypher operators and capabilities in detail.

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Ninety two ] Chapter four consequently, believe that we use a profiler, that's particular to our programming language (either Java, personal home page, Ruby, or something else), and we chanced on that the code executing the next question is especially gradual: MATCH(n:User {email:'user300@learningcypher. com'}) go back n we'd like the way to profile this question to appreciate the operation made through the Cypher engine and its functionality expense in order that we will act to minimize the full price. Profiling utilizing the Java API If we have been operating with Neo4j Embedded, we'd have a code that appears just like the following: import org. neo4j. cypher. javacompat. ExecutionEngine; import org. neo4j. cypher. javacompat. ExecutionResult; // ... extra code String question = "MATCH(n:User {email: {emailQuery}}) go back n"; Map params = new HashMap<>(); params. put("emailQuery", "user300@learningcypher. com"); ExecutionResult outcome = engine. execute(query, params); Neo4j presents how to review the functionality of a question. The ExecutionEngine category has the profile procedure, which returns an ExecutionResult item with profiling data. Invoke it within the related approach you name the execute item. this is often defined within the following code snippet: ExecutionResult outcome = engine. profile(query, params); the one distinction is that we will now invoke the executionPlanDescription approach to the ExecutionResult item. this system is usually found in the ExecutionResult classification yet could be invoked just once the question is profiled, no longer while the question is simply performed; calling this technique after an execute name will reason an exception to be thrown. Now, ponder the subsequent question: end result. dumpToString(); PlanDescription planDescription = end result. executionPlanDescription(); We referred to as the dumpToString functionality prior to getting access to the execution plan since it is on the market purely after the result's enumerated. From bankruptcy 1, Querying Neo4j successfully with development Matching, you are going to do not forget that the ExecutionResult example will be enumerated in 3 ways: through both the dumpToString or columnAs features or an iterator functionality. Whichever you employ is ok, you simply have to enumerate the end result prior to asking for the execution plan. [ ninety three ] Improving functionality contained in the execution plan description The execution plan informs us in regards to the operations made via the execution engine to accomplish our question. we will unload the string illustration of the plan. this is often defined within the following code snippet: approach. out. println(res. executionPlanDescription(). toString()); For the MATCH(n:User {email: {emailQuery}}) go back n question, we get the next consequence: Filter(pred="Property(n,email(0)) == {emailQuery}", _rows=1, _db_hits=1000) NodeByLabel(identifier="n", _db_hits=0, _rows=1000, label="User", identifiers=["n"], producer="NodeByLabel") What do we assemble from this end result? we now have the subsequent steps: • NodeByLabel: This step signifies that the Cypher engine chosen all nodes utilizing a label; the label after all is person and the identifier to check is n. This operation processed a thousand rows (all the clients we've in our database).